Input File Specification for Face Liveness Detection

Image Specification
The input image should be a frontal selfie.
frontal selfie
All atributes on the image is calculated on the passive liveness check. Please input the original image without any cropping and without any enhancement on the camera filter or photoshopped, because we need more context on passive liveness evaluation.
The liveness detection works best in such environments:
On a uniform background
Good image quality such as: not dark, sharp or not blurred, has sufficient contrast, not overshadowed, and no spotlight present on the face.
No wearables occluded the face
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Passive Liveness Requirement Check
Image Input Specification
The submitted image input should fulfill the minimum requirements below:
Image Setting
Recommendation
Minimum camera pixel
Above 2 MP
Image file size
Minimum size 100 KB and the maximum are 2 MB
Image compression recommendation
Bicubic, with minimum JPEG quality 80%
Face Appearance Check: Face Detection and Pose Check
The submitted Face input should fulfill the minimum requirements below:
Image Setting
Text
Minimum face area size
300 x 300 pixels, or adjust on 600 x 600 pixels 
Ideal face posture 
Frontal face 
Face orientation position 
Vertical 
Total face in the frame
1 face
Before processing the image, we will ensure there is a face object on the frame using face detection. If the image input has multiple faces, the algorithm will choose the main bigger face and will check the face pose using head poses analysis to make sure that a face object is in frontal face position.
The algorithm will detect the main bigger face on the frame
The algorithm will detect the main bigger face on the frame
The head pose estimation is needed on this part to prevent over tilted pose that might be reduced prediction accuracy for face occlusion and liveness check. On the other side, the head pose check might help to reject some spoof from paper or replay video.
Face Appearance Check: Face Occlusion Check
face occlusion is an algorithm to check whether the face is occluded by an object or any wearables. The facial areas that is checked by the occlusion are:
Forehead 
Left and right eyebrow 
Left and right eyes 
Left and right cheeks 
Nose 
Mouth
Image Quality Assessment
The image quality assessment will check the quality of the face area. Attributes that are checked by the algorithm consist of:
Sharpness: an image quality attribute to evaluate whether the face area is blurred or not. The value below 0.1 indicates a very blur image, the value between 0.1 to 0.2 indicates doubt are, and the best condition of the sharpness is above 0.2.
Contrast: an image quality attribute to evaluate the face area has enough contrast conditions. The value below 0.5 indicates low contrast,  the value between 0.5 to 0,6 indicates a doubt condition, and enough contrast should be above 0.6.
Brightness: an image quality attribute to evaluate whether the face area has enough lightning condition. The value below 0.3 indicates a very dark condition, the value between 0.3 to 0.4 indicates a doubtful condition, and the best brightness condition is above 0.4.
Face Quality Attribute
Reject
Doubt
Accept
Sharpness 
s < 0.1 
0.1 ≤ s < 0.2 
s ≥ 0.2 
Contrast
s < 0.5 
0.5 ≤ s < 0.6 
s ≥ 0.6 
 Brightness
s < 0.3
0.3 ≤ s < 0.4
s ≥ 0.4
Spoof Component Check
For this liveness detection, the algorithm logic will return 3 recommendations for accepting or rejecting a selfie. The spoof component will check the specific defined obvious characteristics of a suspect spoof selfie by defining visual characteristics without giving selfie source information. 
The defined obvious spoof artifacts consist of Nodeflux passive liveness detection has been trained is mentioned below:
Spoof Edge: it’s mostly found on paper and replay attacks which has border characteristic
Deformation: it’s mostly found on a 3D paper mask, cut mask, and 2D paper mask
Artifact: It's mostly found on replay attacks, which gives characteristic moire and reflection. The Moire pattern is a character of obvious pixelated-like pattern that will be found on replay attack from a device screen, LCD, or monitor. Reflection is sometimes found on a replay attack case from a screen photo medium that reflects an image from the device.
Spoof Characteristic
Condition
Example
Border Edge
Obvious Print Attack on Printed paper with border
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Border Edge
Border from device
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Un-natural deformation
Printed Mask paper with fine cut
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Cut Border
Printed Mask paper with border
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Reflection
Obvious on laptop screen attack contain light reflection and shadow reflection
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Reflection
un-Obvious on laptop screen attack contain light reflection
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Moire
Obvious
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Active Liveness iOS SDK 2.0.0

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Input File Specification for OCR

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Image Specification

Passive Liveness Requirement Check